The journey towards digitalization or transformation of the laboratory landscape is an exciting one, but also a complex, demanding and lengthy endeavour. One of the key pillars of any successful enterprise-wide initiatives such as digitalization is the ability to exchange information – ideas, sentiments, plan, requirements, etc. – freely and precisely among one another with confidence that context and meaning will not be lost.

 

An R&D transformation project involves many specialized roles that can be broadly classified into 2 camps: Science-focused roles such as R&D Leads, Lab Scientists, Regulatory and Compliance Experts and Scientific Business consultants; and Technology-driven roles such as Project Managers, Digital Architects, Data Engineers, Database Architects and Programmers.

 

Most of the project team members need to know and speak either the language of science or technology. However, a few roles such as Consultants, Architects need to speak and understand both parlance and act as the bridge between the 2 camps and ensure information is not lost during translation of scientific needs into technical constructs and vice versa.

 

Often, the communication challenges in bringing the scientific and technology landscapes together are significant enough to impact the team from realizing the project goals. Here we highlight some of the common pitfalls project teams should consider and manage properly in their communication strategy.

 

 

Exercise caution when using acronyms & abbreviations:

Use of IT terms, especially abbreviations and acronyms should be limited and referenced adequately. Most scientists may not be well versed with technical jargons, even those that are considered common and have been there for a long time in the technology world. E.g.  AWS, MVP, FRS, UAT, SQL, JSON, SDLC, Agile. When using acronyms & abbreviations in documents, ensure the glossary section captures the full form of these. In conversations, spell out the full form so you can be sure the audience understands the term correctly.

 

 

Avoid the use of ambiguous terms:

Many words and abbreviations mean different ideas in different domains and also are understood differently by the audience based on context. It is important that team members are aware of the potential risk of misunderstanding and mitigate it. For example, API means Application Programming Interface in IT domain but means Active Pharmaceutical Ingredient in scientific domain.  Similarly, storage is used to refer to Data Servers and Cloud Storage by technology team members, but to a lab scientist storage means cold refrigerators or walk-in freezers and ambient stores. A third example is the term IP. It could mean “Intellectual Property” or “Intraperitoneal” in scientific domain and “IP address” in IT domain.

 

 

Use precise scientific terms:

Scientific informatics consultants need to have a solid understanding of scientific processes and workflows as well as the capabilities and features of the informatics systems that are used. An important part of that knowledge is the proficiency in using precise scientific terms while interacting with scientists. Proper scientific vernacular enables a productive and engaging conversation that helps the scientist understand and respond quickly; for example, when working with Medicinal Chemists, it is appropriate to refer to drug candidates as compounds while with Biologists, drug candidates should be referred to as Antibodies or Proteins.

 

 

Make sure you have a conversation

Scientists should not be expected to translate scientific needs into technical requirements. All attempts should be made to optimize the use of Scientists’ time. Engaging in a conversation, where there is a true exchange of valuable information both ways, helps understand underlying needs of the Scientist. Open-ended questions like “Could you please explain your process?” often take the conversation in multiple directions and fail to keep it focussed. Instead, direct questions like “How do you receive your samples?” or “How do you prepare samples for analysis?” encourage scientists to describe the lab process from their perspective in detail allowing everyone to understand both the context and the specifics.

 

In summary, approaching informatics systems and data from a scientist’s perspective, engaging in periodic dialogues with scientists on their operations, seeking feedback on communication gaps and challenges help build a strong relationship with the scientific team and bring them closer to the target of the digitalization journey.

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